Home entertainment systems, including television and media centers, are converging with the Internet and providing access to a large number of available sources of content, such as video, movies, TV programs, music, etc. This expansion in the number of available sources necessitates a new strategy for navigating a media interface associated with such systems and making content recommendations and selections.
The large number of possible content sources creates an interface challenge that has not yet been successfully solved in the field of home media entertainment. This challenge involves successfully presenting users with a large number of elements (programs, sources, etc.) without the need to tediously navigate through multiple display pages or hierarchies of content.
Further, most existing search paradigms make an assumption that the user knows what they are looking for when they start, whereas often, a mechanism to allow a process of discovery and cross linkage is more desirable or appropriate.
One approach for allowing a process of discovery and cross linkage is the use of ratings. Under this approach a user rates content and a recommendation engine recommends additional content related to the rated content. For example, if a user gives an action movie a five star rating and a horror movie a one star rating, a conventional recommendation engine is likely to recommend other action movies to the user rather than other horror movies. A drawback to this approach is that recommendations tend to be skewed to particular movie genres until a large enough rating database is created over multiple movie genres (e.g., action, horror, romance, etc.) by the user. As should be appreciated, the creation of such a rating database has the drawback of being time consuming. Furthermore, another drawback is that even if a large rating database is created by a user, there still may be inaccurate or non-relevant recommendations since the rating information may have been inaccurately collected from the user. For example, if a user rates the first five horror movies presented for rating as one star movie, the conventional recommendation engine may stop recommending horror movies to the user. However, the user may just not have liked the first five horror movies presented and may actually desire to have other horror movies brought to his or her attention.
Another approach for allowing a process of discovery and cross linkage is the tracking a user's viewing/purchasing habits over a period of time. However, similar to the creation of a rating database, tracking user's viewing/purchasing habits over a long enough time period to generate relevant recommendations also has the drawback of being time consuming.
The present disclosure is directed towards overcoming these drawbacks.